Find research datasets worth reusing

opencc-by-4.0Nov 2022View details →

The role of the intraspecific variability of hydraulic traits for modelling the plant water use in different European forest ecosystems: scripts, model output, and parameter files

This repository contains the model outputs and R scripts used to process the data to analyze the impact of the plant hydraulic parameterization of the manuscript: "The role of the intraspecific variability of hydraulic traits for modelling the plant water use in different European forest ecosystems". The following is a detailed description of the content of this repository: model_output.zip: This compressed file contains the results of all the individual numerical experiments per experimental site as produced by the Comunity Land Model version 5. The files are stored in NETCDF format per year. The folder is arranged with subfolders containing the individual results from each experimental site as follows: <ul> <li>rc: model output with the results of the resistant configuration of experiment 1 (RC)</li> <li>vc: model output with the results of the vulnerable configuration of experiment 1 (VC)</li> <li>k_dc: model output with the results of the default configuration used for experiments 1 and 2 (DC or DCkmax)</li> <li>k_rc: model output with the results of the low plant hydraulic conductance (Lkmax) for experiment 2</li> <li>k_irc: model output with the results of the intermediate low plant hydraulic conductance (ILkmax) for experiment 2</li> <li>k_vc: model output with the results of the high plant hydraulic conductance (Hkmax) for experiment 2</li> <li>k_ivc: model output with the results of the intermediate high plant hydraulic conductance (IHkmax) for experiment 2</li> <li>k_iirc: model output with the results of the additional intermediate low plant hydraulic conductance (IILkmax) for experiment 2</li> <li>ko_dc: model output with the results of the best kmax and the default configuration of the PVC used in experiment 3</li> <li>ko_rc: model output with the results of the best kmax and the resistant configuration of the PVC used in experiment 3</li> <li>ko_vc: model output with the results of the best kmax and the vulnerable configuration of the PVC used in experiment 3</li> </ul> The scripts were written for use in RStudio, and each contains a detailed description of the data requirements and outputs. Each script was developed to read directly the netcdf files of the model output and the csv files containing the transpiration estimates calculated from the SAPFLUXNET per experimental site (script 1).

Model output from CAABA/MECCA study "Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)"

This dataset includes the main data obtained during the study "Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)" (DOI:10.5194/gmd-2023-102). The updated model code can be found at zenodo.org (DOI:10.5281/zenodo.7944174). The data can be used to replicate the results shown in the manuscript. Contained are results produced by the updated CAABA/MECCA (version 4.7.0) and reference data from CAABA/MECCA version 4.5.5. In version 4.7.0, new biogenic and anthropogenic species are introduced to the model (limonene and long-chained alkanes) with refined multiphase chemistry, while new reaction pathways are added for existing compounds (isoprene, benzene and IEPOX). The output is generated to evaluate model results in terms of temperature- and NOx-dependency.

Input and output data (images + boulder labels, model setup, model weights and more) for the manuscript "Automatic characterization of boulders on planetary surfaces from high-resolution satellite images"

File 1: raw_data_BOULDERING.zip Size: 8.8 GB Summary: It contains all of the rasters (planetary images) and labeled boulders (raw data): <ul> <li> a boulder-mapping file, which is the manually digitized outline of boulders. </li> <li> a ROM file (stands for Region of Mapping), which depicts the image patches on which the boulder mapping has been conducted. </li> <li> a global-tiles file, which shows all of the image patches within a raster. </li> </ul> There are multiple locations/images per planetary body. Structure: <pre>. └── raw_data/ ├── earth/ │ └── image_name/ │   ├── shp/ │   │ ├── <image_name>-ROM.shp │   │ ├── <image_name>-boulder-mapping.shp │   │ └── <image_name>-global-tiles.shp │   └── raster/ │     └── <image_name>.tif ├── mars/ │ └── image_name/ │   ├── shp/ │   │ ├── <image_name>-ROM.shp │   │ ├── <image_name>-boulder-mapping.shp │   │ └── <image_name>-global-tiles.shp │   └── raster/ │     └── <image_name>.tif └── moon/   └── image_name/     ├── shp/     │ ├── <image_name>-ROM.shp     │ ├── <image_name>-boulder-mapping.shp     │ └── <image_name>-global-tiles.shp     └── raster/       └── <image_name>.tif</pre>   File 2: best_model.zip Size: 624.7 MB Summary: This zip file contains all of the inputs and outputs required/obtained from the training of the BoulderNet Mask R-CNN model (model setup, augmentation pipeline, model weights, log during training, logged metrics): <ul> <li> augmentation_pipeline.json (required as inputs for the training of the algorithm to apply augmentations). See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information. </li> </ul> <ul> <li> Base-RCNN-FPN.yaml (base model setup file). </li> <li> config.yaml (complete model setup file, merge of the base and Mars-Moon-Earth setup file). </li> <li> Mars-MoonEarth-v050...yaml (model setup file). </li> <li> log.txt (log during training of the algorithm). </li> <li> model_0055999.pth (model weights at second last saving step) </li> <li> model_0063999.pth (model weights at last saving step) </li> </ul> We advice the use of model weights model_0055999.pth (to avoid slight overfitting). File 3: Apr2023-Mars-Moon-Earth-mask-5px.zip (pre-processed input images) Size: 252.8 MB Summary: This zip files contains the input data (images and boulder outlines) for the train, validation and test datasets. See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information in how-to-use the different files. <ul> <li> The json folder contains json files that can be given as input (as a custom dataset) to the Detectron2 platform. The only differences between the two files is how the bounding boxes around masks have been generated. We advised to use "Apr2023-Mars-Moon-Earth-mask-5px.json". </li> <li> The pkl folder and pickle file includes some informations about the 950 image patches in our boulder dataset. </li> <li> The pre-processing folder contains all of the training, validation and test image patches and corresponding shapefiles. </li> <li> The shapefile folder is actually empty (it should not be there!). </li> </ul> Structure: <pre>. └── preprocessed_inputs/   ├── json   ├── pkl   ├── preprocessing/   │   ├── train/   │   │   ├── images   │   │   └── labels   │   ├── validation/   │   │   ├── images   │   │   └── labels   │   └── test/   │       ├── images   │       └── labels   └── shp</pre>

The coupled ice sheet-Earth system model Bern3D v3.0: Model output

This dataset contains model output of climate and ice sheet variables for the simulations performed in the study: Pöppelmeier, F., Joos, F., Stocker, T. F. (2023). The coupled ice sheet-Earth system model Bern3D v3.0. Journal of Climate. 2D and 3D output variables are available for the preindustrial (PI) and Last Glacial Maximum (LGM) control simulations. Timeseries output is provided for CO2 experiments for which CO2 concentrations were increased to 2 and 4 times PI concentrations with rates of 0.5, 1, and 2% per year. Timeseries output is also provided for the simulation of the entire last glacial cycle in the standard setup and with logarithmically scaled dust for the aerosol radiative forcing. More details are provided in the above mentioned manuscript.

Model outputs for the study "Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis"

This resources provides pre-processed input data, model checkpoints and model outputs for experiments on the OpenI dataset in below study.  <blockquote> Jan Trienes, Paul Youssef, Jörg Schlötterer, and Christin Seifert. 2023. <a href="https://arxiv.org/abs/2307.12803">Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis</a>. In Proceedings of the 16th International Natural Language Generation Conference (INLG), Prague, Czech Republic. Association for Computational Linguistics. </blockquote> For more information please refer to the accompanying paper and code repository (<a href="https://github.com/jantrienes/inlg2023-radsum">https://github.com/jantrienes/inlg2023-radsum</a>). The data is structured as follows: <ul> <li><code>data/preprocessed/</code> includes the dataset(s) for each model</li> <li><code>output/</code> includes one folder for each experiment/model run. The first part of each output path indicates the dataset that was used at inference.</li> <li>For a mapping between model IDs and results in the paper, see below table. All models were also trained with the background section as input. These are available in directories with the <code>-bg-</code> qualifier. </li> </ul> <table> <thead> <tr> <th>Model name in paper</th> <th>Output directory</th> </tr> </thead> <tbody> <tr> <td>Results from Table 2</td> </tr> <tr> <td>OracleExt</td> <td>openi-unguided/oracle</td> </tr> <tr> <td>BertExt (Liu and Lapata, 2019)</td> <td>openi-unguided/bertext-default</td> </tr> <tr> <td>BertAbs (Liu and Lapata, 2019)</td> <td>openi-unguided/bertabs-default</td> </tr> <tr> <td>GSum (Dou et al., 2021)</td> <td>openi-bertext-default-clip-k1/gsum-default</td> </tr> <tr> <td>GSum w/ LR-Approx</td> <td>openi-bertext-default-clip-lrapprox/gsum-default</td> </tr> <tr> <td>GSum w/ BERT-Approx</td> <td>openi-bertext-default-clip-bertapprox/gsum-default</td> </tr> <tr> <td>GSum w/ Thresholding</td> <td>openi-bertext-default-clip-threshold/gsum-default</td> </tr> <tr> <td>WGSum (Hu et al., 2021)</td> <td>openi-wgsum/wgsum-default</td> </tr> <tr> <td>WGSum+CL (Hu et al., 2022)</td> <td>openi-wgsum-cl/wgsum-cl-default</td> </tr> <tr> <td>Results from Table 3</td> </tr> <tr> <td>Fixed (k=1)</td> <td>openi-unguided/bertext-default-clip-k1</td> </tr> <tr> <td>LR-Approx</td> <td>openi-unguided/bertext-default-clip-lrapprox</td> </tr> <tr> <td>BERT-Approx</td> <td>openi-unguided/bertext-default-clip-bertapprox</td> </tr> <tr> <td>Thresholding</td> <td>openi-unguided/bertext-default-clip-threshold</td> </tr> <tr> <td>k = |OracleExt|</td> <td>openi-unguided/bertext-default-clip-oracle</td> </tr> <tr> <td>Results from Table 4</td> </tr> <tr> <td>Fixed (Dou et al., 2021)</td> <td>openi-bertext-default-clip-k1/gsum-default</td> </tr> <tr> <td>Oracle Length</td> <td>openi-bertext-default-clip-oracle/gsum-default</td> </tr> <tr> <td>Oracle Length + Content</td> <td>openi-oracle/gsum-default</td> </tr> <tr> <td>Results from Table 5</td> </tr> <tr> <td>BertExt w/ k=[1,5]</td> <td>openi-unguided/bertext-default-clip-k{1,2,3,4,5}</td> </tr> <tr> <td>GSum w/ k=[1,5]</td> <td>openi-bertext-default-clip-k{1,2,3,4,5}/gsum-default</td> </tr> </tbody> </table>

Bern3D model output data from idealized co2 increase-decrease simulations to investigate reversibility in the Earth system

The data described below is output from the Bern3D intermediate complexity model and idealized CO2 increase-decrease simulations to investigate reversibilty and hysteresis for different maximum co2 forcings. The data are provided as .csv and .nc files The first row in the .csv files contains the header, which describes the variable. The naming convention is as follows: c#k#_VARIABLE c# indicates the maximum co2 as times pre-industrial (c2 to c5) k# indicates the equilibrium climate sensitivity of the respective simulation in degrees C (k2 to k5) and VARIABLE indicates the value of the respective variable, which are:     co2:        change in atmospheric co2 concentration in [ppm]     amoc:        change in maximum of the Atlantic meridional overturning circulation in [Sv]     ohc:        change in ocean heat content in [10^24 J]     seaice:    sea-ice area remaining as fraction of the pre-industrial cover     Om_arag:    fraction of water with Omega_arag > 3 in the upper 175 m     o2_thermo:    change in thermocline (200-600 m) oxygen concentration in [mmol m^-3] for each variable a separate file exists where the variable and co2 are provided. Spatial data to create the maps of hysteresis on a grid-cell basis are provided for the two scenarios as .nc files. The naming is as follows: c#k#_hyst_o2thermo.nc where c# corresponds again to maximum co2 as times pre-industrial and k# to the equilibrium climate sensitivity. The .nc files contain the coordinate (latitude, longitude) centers (lat_t, lon_t) and edges (lat_u, lon_u) as well as the hysteresis area (hystA_o2thermo) in [mmol m^-3]. The files can be readily importet in python, for example, by:     import pandas as pd     import xarray as xr          # for the .csv files     df = pd.read_csv('path+filename', sep=',', header=0, index_col=None)          # for the .nc files     ds = xr.open_dataset('path+filename') For additional information or in case of questions please contact: Aurich Jeltsch-Thömmes aurich.jeltsch-thoemmes@unibe.ch

Bern3D model output related to: Hysteresis of the Earth system under positive and negative CO2 emissions

The data below is output from the Bern3D intermediate complexity model and idealized CO2 increase-decrease simulations used in Jeltsch-Thömmes et al., Environ. Res. Lett. 15 (2020) 124026, https://doi.org/10.1088/1748-9326/abc4af The data are provided as .csv and .nc files There are different types of data 1) TIMESERIES DATA (Fig. 1 and 2) ================================= The name of the files indicates the variable:     co2_ts.csv            change in atm. co2 [ppm]     cumulativeEmissions_ts.csv    cumulative emissions [GtC]     cumulativeAOflux_ts.csv    cumulative atm-ocean C flux [GtC]     cumulativeABflux_ts.csv    cumulative atm-land C flux [GtC]     sat_ts.csv            change in surface air temperature [degC]     ohc_ts.csv            change in ocean heat content [10^24 J]     amoc_ts.csv            change in Atlantic meridional overturning circulation strength [Sv]     seaice_ts.csv            fraction of pre-industrial sea-ice area remaining [fraction of PI]      The first row in the .csv files contains the header, which indicates the experiment. The naming convention is as follows: c4k#_### c4 indicates the maximum co2 as times pre-industrial (4 times) k# indicates the equilibrium climate sensitivity of the respective simulation in degrees C (k2 to k5) ### indicates the rate of CDR:     010:    0.1% yr^-1     010:    0.3% yr^-1     010:    0.5% yr^-1     010:    0.7% yr^-1     100:    1% yr^-1     200:    2% yr^-1     400:    4% yr^-1     600:    6% yr^-1 2) HYSTERESIS DATA (Fig. 3) =========================== The name of the files indicates the variables:     cumulativeEmissions_sat.csv        cumulative emissions and change in surface air temperature [degC]     cumulativeEmissions_OHCsurf.csv    cumulative emissions and change in upper ocean heat content (0-700 m) [10^24 J]     cumulativeEmissions_o2thermo.csv    cumulative emissions and change in thermocline (200-600 m) o2 [mmol m^-3]     cumulativeEmissions_OM_arag.csv    cumulative emissions and fraction of water in the uppermost 175 m with omegar_aragonite saturation state >3 [fraction] each file contains the time (simulation year) as well as cumulative emissions (cumuEmis) and the respective variable (same naming as in filename) for all the experiments (see timeseries data for naming convention) 3) SPATIAL DATA (Fig. 4 and 5) ============================== All data for Fig. 4 and 5 are contained in one single .nc file (fig4_5_data.nc) with a varibale for each map shown in Fig. 4 and 5:     c4k2_100_sat        hysteresis (down-path minus up-path) in surface air temperature at cumulative emissions of 1000 GtC, ECS=2 degC, in [degC]     c4k3_100_sat        hysteresis (down-path minus up-path) in surface air temperature at cumulative emissions of 1000 GtC, ECS=3 degC, in [degC]     c4k5_100_sat        hysteresis (down-path minus up-path) in surface air temperature at cumulative emissions of 1000 GtC, ECS=5 degC, in [degC]          c4k2_100_o2thermo    hysteresis (down-path minus up-path) in thermocline (200-600 m) o2 at cumulative emissions of 1000 GtC, ECS=2 degC, in [mmol m^-3]     c4k3_100_o2thermo    hysteresis (down-path minus up-path) in thermocline (200-600 m) o2 at cumulative emissions of 1000 GtC, ECS=3 degC, in [mmol m^-3]     c4k5_100_o2thermo    hysteresis (down-path minus up-path) in thermocline (200-600 m) o2 at cumulative emissions of 1000 GtC, ECS=5 degC, in [mmol m^-3]          c4k3_100_Om_arag_up    mean aragonite saturation state of the uppermost 175 m at cumulative emissions of 1000 GtC on the up-path, ECS=3 degC, [unitless]     c4k3_100_Om_arag_do    mean aragonite saturation state of the uppermost 175 m at cumulative emissions of 1000 GtC on the down-path, ECS=3 degC, [unitless]     The files can be readily importet in python, for example, by:     import pandas as pd     import xarray as xr          # for the .csv files     df = pd.read_csv('path+filename', sep=',', header=0, index_col=None)          # for the .nc files     ds = xr.open_dataset('path+filename') For additional information or in case of questions please contact: Aurich Jeltsch-Thömmes aurich.jeltsch-thoemmes@unibe.ch

Outputs of the Jupyter Notebook - Learning the Underlying Physics of a Simulation Model of the Ocean's Temperature (CIRC23)

The dataset contains the outputs of the notebook "Learning the Underlying Physics of a Simulation Model of the Ocean's Temperature (CIRC23)" published in The Environmental Data Science Book.

Output tomographic models for "The attenuation and scattering signature of fluids and tectonic interactions in Central-Southern Apennine."

Output ASCII file for the seismic attenuation tomography in Central-Southern Apennines. The output format is the one from MuRAT software (De Siena et al.,  2014). Q and Peak-Delay models in 1.5 Hz, 3 Hz and 6 Hz frequencies are reported as specificated by the files name. The output points of a grid with coordinates available in  WGS84 degrees ("Degrees" suffix) or already projected in kilometric UTM coordinates ("UTM" suffix). All other information can be found in the main and supplementary text.

openCC (other)Feb 2022View details →

edi44/100

Model output, drivers and parameters for Ecosystem Recovery from Disturbance is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance Between Vegetation and Soil-Microbial Processes

Files used to generate the data for figures in: Rastetter, EB, Kling, GW, Shaver, GR, Crump, BC, Gough, L. Ecosystem Recovery from Disturbance Is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance between Vegetation and Soil-Microbial Processes. Ecosystems (2020). https://doi.org/10.1007/s10021-020-00542-3. This paper present a framework for assessing biogeochemical recovery of terrestrial ecosystems from disturbance. We identify three recovery phases. In Phase 1, nitrogen is redistributed from soil organic matter to vegetation, but the ecosystem continues to lose nitrogen because the recovering vegetation cannot take up nitrogen as fast as it is released from soil. In Phase 2, the ecosystem begins re-accumulating nitrogen and converges on a quasi-steady state in which vegetation and soil-microbial processes are in balance. In Phase 3, vegetation and soil-microbial processes remain in balance and the ecosystem slowly re-accumulates the remaining nitrogen.

opencc-by-4.0Mar 2020View details →

Model outputs for validation and inference of high‐resolution information (downscaling) of ENETwild abundance model for wild boar, January 2020 update

These maps are models obtained in intermediate phases of the ENETWILD project based on available information. There are frequent updates in order to improve the results. Objectives: - Validation of previously produced hunting yield maps and new ones - Downscaling to 10x10 km grid >>> file  "January_2020_HY_nut01_10x10.tif" - Downscaling to 2x2 km grid   >>> file "January_2020_HY_nut00_2x2.tif" Model settings and predictors:     - Assuming cells as municipality in 10x10 km grid downscaling - Assuming cells as hunting grounds in 2x2 km grid downscaling     Conclusions guiding future methodological steps: - To update wild boar hunting yield data for some specific regions - To increase hunting yield data resolution - To explore model independent parametrization for each bioregion For further details and methodological approach see the paper: ENETWILD-consortium, P. Acevedo, S .Croft, G C Smith, J. A. Blanco-Aguiar, J. Fernandez-Lopez, M. Scandura, M. Apollonio, E.Ferroglio, Oliver Keuling, M. Sange, S. Zanet, F. Brivio, T. Podgórski, K.Petrović, G. Body, A.  Cohen, R. Soriguer, J. Vicente (2020) Validation and inference of high-resolution information (downscaling) of ENETwild abundance model for wild boar. EFSA supporting publication 2020:EN-1787. 23pp. doi:10.2903/sp.efsa.2020.EN-1787. Permission for reuse hunting yield outputs is granted under the terms indicated  by EFSA.

opencc-by-4.0Mar 2020View details →

Model outputs for occurrence and hunting data‐based models of wild boar distribution and abundance, July 2019 update

These maps  are wild boar habitat suitability outputs based on newly available data of wild boar, and models for predicting wild boar relative abundance using hunting yields. Objectives: - Validation of previously produced hunting yield maps and new ones - Downscaling to 10x10 km grid - Downscaling to 2x2 km grid Model settings and predictors:     - Model from ENETWILD report August 2019 - Assuming cells as municipality in 10x10 km grid downscaling - Assuming cells as hunting grounds in 2x2 km grid downscaling     Conclusions guiding future methodological steps - To update wild boar hunting yield data for some specific regions; - To increase hunting yield data resolution; - To explore model independent parametrization for each bioregion. Files: August_2019_HY_nut00_10x10        >> Model outputs based on hunting yield GLM analyses August_2019_occurrences_bioclim   >> Model outputs based on Bioclim analyses August_2019_occurrences_glm         >> Model outputs based on Generalised linear model August_2019_occurrences_ksvm      >> Model outputs based on Support vector Machine analyses August_2019_occurrences_maxent   >> Model outputs based on Maxent analyses August_2019_occurrences_randomForest>> Model outputs based on Random Forest analyses --------------------------------------------------------------------------------------------------------------------------------------------------------- These maps are models obtained in intermediate phases of the ENETWILD project based on available information.  There are frequent updates in order to improve the results. For methodological approach and details check the paper:  ENETWILD‐consortium, P. Acevedo, S .Croft, G C Smith, J. A. Blanco-Aguiar, J. Fernandez-Lopez, M. Scandura, M. Apollonio, E.Ferroglio, Oliver Keuling, M. Sange, S. Zanet, F. Brivio, T. Podgórski, K.Petrović, G. Body, A.  Cohen, R. Soriguer, J. Vicente (2019). ENETwild modelling of wild boar distribution and abundance: update of occurrence and hunting data‐based models. EFSA Supporting Publications, 16(8), 1674E. <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fefsa.onlinelibrary.wiley.com%2Fdoi%2Fabs%2F10.2903%2Fsp.efsa.2019.EN-1674&data=02%7C01%7C%7Ca8ad922eefde42f5cb5208d7c5054851%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C637194498792136402&sdata=fqdiYEOqYIlHaDbp5a7kVdGQ6FWuFEydJNhSWOghH%2FQ%3D&reserved=0">https://efsa.onlinelibrary.wiley.com/doi/abs/10.2903/sp.efsa.2019.EN-1674</a> . Permission for reuse occurrence  outputs records is granted under the terms of a CC-BY-NC license. Permission for reuse hunting yield outputs is granted under the terms indicated by EFSA.

opencc-by-4.0May 2020View details →

Model outputs for update of occurrence and hunting yield-based data models for wild boar at European scale: new approach to handle the bioregion effect, May 2020 update

These maps are models obtained in intermediate phases of the ENETWILD project based on available information. There are frequent updates in order to improve the results. Objectives: - Incorporate additional data to provide new maps of wild boar suitability with a resolution of 2x2 km >>> file 3_June_2020_suitability_2x2.tif - New model based on hunting yield with different approaches to handle the biorregion effect >>> files 1_June_2020_HY_nut01_10x10_twostep.tif  &  2_June_2020_HY_nut01_10x10_pca.tif Model settings and predictors:     - Hunting yield modeling including biorregion effect as bioclimatic PCA scores - Hunting yield addressing biorregion effect in a two-step procedure with independent parametrization for each bioregion Conclusions guiding future methodological steps: - For wild boar suitability maps at 2x2 km, additional data on survey effort is critical in the southern bioregion - Hunting yield model predictions at 10x10 km grids overestimated the hunting bag numbers obtained from the external datasets - HY model with independent parametrization for each bioregion performed better that previous and new strategies For further details and methodological approach see the paper: ENETWILD-consortium, P. Acevedo, S .Croft, G C Smith, J. A. Blanco-Aguiar, J. Fernandez-Lopez, M. Scandura, M. Apollonio, E.Ferroglio, Oliver Keuling, M. Sange, S. Zanet, F. Brivio, T. Podgórski, K.Petrović, Soriguer, J. Vicente (2020) update of occurrence and hunting yield-based data models for wild boar at European scale: new approach to handle the bioregion effect. EFSA supporting publication 2020 TO BE COMPLETED Permission for reuse hunting yield outputs is granted under the terms indicated  by EFSA.

opencc-by-4.0Oct 2020View details →

Model outputs for the paper "Drastic Vegetation Change in the Guajira Peninsula (Colombia) during the Neogene" submitted to Paleoceanography and Paleoclimatology

We provide the netcdf files used to plot precipitation figures over South America in the paper. Four files are provided. Three files are for "preindustrial", "no Andes" and "open Central American Seaway" experiments that were carried out with the fully coupled model IPSL-CM4 (Marti et al., 2010). One file contains the outputs from a Miocene simulation carried out with NCAR CESM in Zhou et al. (2018). References : Marti, Olivier, P. Braconnot, J.-L. Dufresne, J. Bellier, R. Benshila, S. Bony, P. Brockmann, et al. 2010. « Key Features of the IPSL Ocean Atmosphere Model and Its Sensitivity to Atmospheric Resolution ». Climate Dynamics 34 (1): 1‑26. https://doi.org/10.1007/s00382-009-0640-6. Zhou, Haoran, Brent R. Helliker, Matthew Huber, Ashley Dicks, et Erol Akçay. 2018. « C4 Photosynthesis and Climate through the Lens of Optimality ». Proceedings of the National Academy of Sciences 115 (47): 12057‑62. https://doi.org/10.1073/pnas.1718988115.

opencc-by-4.0Jul 2019View details →

Model outputs to "Langtang Ice Cliff Modelling Study"

This dataset contains all the model results of our study "Supraglacial ice cliffs can substantially increase the mass loss of debris-covered glaciers". The zip-file contains one main folder: "Cliffmodel_Results" (produced using the 3D-backwasting ice cliff model from Buri et al., 2016b, Buri & Pellicciotti, 2018). More raw data upon request.   Folder structure of "Langtang_IceCliffModellingStudy.7z": ───Cliffmodel_Results    ├───LangshishaGlacier    │   ├───Output_Cliffs_Langshisha2014_A_3m    │   ├───Output_Cliffs_Langshisha2014_B_3m    │   ├───Output_Cliffs_Langshisha2014_C_3m    │   ├───Output_Cliffs_Langshisha2014_D_3m    │   ├───Output_Cliffs_Langshisha2014_E_3m    │   ├───Output_Cliffs_Langshisha2014_F_3m    │   ├───Output_Cliffs_Langshisha2014_G_3m    │   └───Output_Cliffs_Langshisha2014_H_3m    ├───LangtangGlacier    │   ├───Output_Cliffs_Langtang2014_A_3m    │   ├───Output_Cliffs_Langtang2014_B_3m    │   ├───Output_Cliffs_Langtang2014_C_3m    │   ├───Output_Cliffs_Langtang2014_D_3m    │   ├───Output_Cliffs_Langtang2014_E_3m    │   ├───Output_Cliffs_Langtang2014_F_3m    │   ├───Output_Cliffs_Langtang2014_G_3m    │   ├───Output_Cliffs_Langtang2014_H_3m    │   ├───Output_Cliffs_Langtang2014_I_3m    │   ├───Output_Cliffs_Langtang2014_J_3m    │   ├───Output_Cliffs_Langtang2014_K_3m    │   ├───Output_Cliffs_Langtang2014_L_3m    │   ├───Output_Cliffs_Langtang2014_M_3m    │   ├───Output_Cliffs_Langtang2014_N_3m    │   ├───Output_Cliffs_Langtang2014_O_3m    │   ├───Output_Cliffs_Langtang2014_P_3m    │   ├───Output_Cliffs_Langtang2014_Q_3m    │   ├───Output_Cliffs_Langtang2014_R_3m    │   ├───Output_Cliffs_Langtang2014_S_3m    │   └───Output_Cliffs_Langtang2014_T_3m    ├───LirungGlacier    │   ├───Output_Cliffs_Lirung2014_A_3m    │   │   └───intermediateOutput    │   └───Output_Cliffs_Lirung2014_B_3m    │       └───intermediateOutput    └───ShalbachumGlacier        ├───Output_Cliffs_Shalbachum2014_A_3m        │   └───intermediateOutput        ├───Output_Cliffs_Shalbachum2014_B_3m        │   └───intermediateOutput        ├───Output_Cliffs_Shalbachum2014_C_3m        │   └───intermediateOutput        ├───Output_Cliffs_Shalbachum2014_D_3m        │   └───intermediateOutput        └───Output_Cliffs_Shalbachum2014_E_3m            └───intermediateOutput     File structure within Cliffmodel_Results \ <GLACIERNAME> \  Output_Cliffs_<GLACIERNAME>2014_A_3m: DC_Fluxes_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m_ls.rda →List of 10 matrices for each energy balance flux with average diurnal cycle (rows) and cliff (columns) for specific model period  geom_ts<TIMESTEP1>_Cliffs_Lirung2014_A_3m.txt →Characteristics (Elevation,Slope,Aspect,x,y,ID,VsI,VsL,Vd) for each pixel of all cliffs (specific ID) for specific model period  MODELSETTINGS_GSM_<GLACIERNAME>_<GLACIERSECTOR>_22Aug_Cliffs_<GLACIER>2014.txt →Details on all modelsettings PC_Melt_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m.txt →Melt values for each cliff (columns) and timestep (rows) for specific model period  PC_Stats_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m.txt →Summary statistics for each cliff (rows) for specific model period  perPx_DC_Fluxes_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m_ls.rda →List of list with per pixel diurnal cycle and standard deviation of each energy flux for specific model period  SD_Fluxes_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m_ls.rda →List of 10 matrices for each energy balance flux with standard deviation for each hour of the day (rows) and cliff (columns) for specific model period  Stats_MeanFluxes_tts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m.txt →Mean energy fluxes per timestep (rows) averaged over all ice cliffs Stats_NonGriddedData_ts<TIMESTEP1>to<TIMESTEP2>_Cliffs_<GLACIERNAME>2014_3m.txt →Statistics of non-distributed parameters averaged over all cliffs per timestep (rows)

opencc-by-4.0Feb 2019View details →

AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL winter wheat simulations

This data set contains output data from simulations with the model LPJmL for winter wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season'). Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.

opencc-by-4.0Feb 2019View details →

AgMIP's global gridded crop model intercomparison (GGCMI) phase II CTWN-A archive: priority 1 outputs from LPJmL spring wheat simulations

This data set contains output data from simulations with the model LPJmL for spring wheat as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase II output data set. Output variables included are: crop yield, above-ground biomass, planting day, maturity day, potential irrigation water withdrawal, actual growing season evapotranspiration . Simulations are based on 31-year simulations using the AgMERRA data set with 4 atmospheric CO2 mixing ratios (C=360, 510, 660, 810 ppm) uniform offsets for temperature (T= -1, 0, 1, 2, 3, 4, 6 K), water (W= -50, -30, -20, -10, 0, 10, 20, 30 %, and infinite/irrigated), and 3 nitrogen input levels (N= 10, 60, 200 kgN/ha) using 2 assumptions on adaptation (A= 'none', 'regain original growing season'). Version 2 of these files has been corrected with respect to the temporal sequence of results, which is not important if looking at 30-year averages as in Franke et al. 2020, but becomes relevant if looking at individual years.

opencc-by-4.0Jun 2021View details →

Data for Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications

These data were generated for the Open-Acces Article : Kamerling, S.; Vuillerme, V.; Rodat, S. Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications. Energies 2021, 14, 3731. https://doi.org/10.3390/en14133731 In these dataset, the data for the Case Study and the Sensitivity Analysis are available. Jupyter Notebooks for further process of these data are also available. The NoteBooks AnalyseHourlyValues, AnalyseDailyValues and AnalyseMonthlyValues allow for easy change of variable, whereas CaseStudyAnalysis is for one specific set of data. The AnalyseSets were created in order to analyse the influence of the optimization on the solar fraction of the different datasets.

opencc-by-4.0Nov 2024View details →

Model output and figure code for "Tradeoffs Between Vertical and Lateral Resilience in a Salt Marsh Restoration Model"

Simulation output data from COLT_Restorations model stored on the Community Surface Dynamics Modeling System public model repository at <a href="https://csdms.colorado.edu/wiki/Model:COLT_Restorations">https://csdms.colorado.edu/wiki/Model:COLT_Restorations</a> and on a GitHub public repository at<a href="https://github.com/mbbarksdale/CoLT_Restorations"> https://github.com/mbbarksdale/CoLT_Restorations</a>. These data were processed and graphed with model code that can also be found in a GitHub public repository at <a href="https://github.com/mbbarksdale/COLTRestorationsPaper_dataANDfigures">https://github.com/mbbarksdale/COLTRestorationsPaper_dataANDfigures</a>.